GAIA benchmark
A 466-question benchmark from Meta + Hugging Face that tests general-purpose AI assistants on real-world tasks requiring web browsing, file handling, and multi-step reasoning.
GAIA (General AI Assistants benchmark) is the leading public eval for general-purpose agents — the kind that need to browse the web, read documents, do math, and combine those into multi-step answers. Each question is grounded in a specific fact a human assistant would research, not in language-only puzzles.
The benchmark has three difficulty levels. Level 1 questions take a human ~5 minutes; Level 3 questions take 5+ minutes of focused research. Manus, GPT-4 with tools, and OpenAI Deep Research lead the leaderboard in 2026, scoring 60–75% accuracy where humans score 92%.
For agent buyers, GAIA scores are a useful sanity check but not a complete picture. They measure generalist competence; they do not measure how well an agent handles your specific domain. Use GAIA to filter the obviously-weak; use your own evals to pick a winner.
Frequently asked
How is GAIA different from SWE-bench?+
SWE-bench tests coding agents on real GitHub issues. GAIA tests general-purpose agents on research/reasoning tasks that mix web browsing, file handling, and math. Different agent class; different ceiling.
Should I trust GAIA scores when picking an agent?+
Use them as a floor. A GAIA score under 30% is a red flag. A score over 60% means the agent can do real general work. But always validate on your specific use case — strong GAIA scores do not guarantee fit.